The Role of Transformational Leadership in Work-Life Balance and Employee Performance: A Post-Pandemic Pilot Study on Singapore Organisations
Bibliographic record
Abstract
Working from home has increasingly become the norm since the outbreak of COVID-19, and as a result, it has taken a toll on both work and family life for many people around the world, including in Singapore. This research aimed to explore the role of transformational leadership in work-life balance and employee performance in Singapore after the pandemic. A pilot study was conducted using a cross-sectional quantitative design and an online survey with 31 participants. The data collected were statistically tested, and it was found that work-life boundary management had a significant positive relationship with both work-life balance and employee performance. In addition, work-life balance was shown to have a significant positive relationship with employee performance. The pilot study did not find any support for the impact of work-life policies and practices on work-life balance or employee performance. Work-life balance did not mediate the relationship between work-life boundary management or work-life policies and practices and employee performance. The moderating effect of transformational leadership was absent in all relationships in the proposed research model. These findings suggest that employees who can manage their work-life boundaries well have better work-life balance and perform better. Organisations should also do their part in facilitating the achievement of even greater work-life balance for their employees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".